Cranial ultrasound for beginners
Bibliographic record
Abstract
Cranial ultrasound (CUS) is an extremely valuable tool to evaluate the brain during the first year of life, in experienced hands. It is the initial screening imaging tool to evaluate the infants' brain and complementary to the use of computed tomography (CT) and magnetic resonance imaging (MRI). It is an accessible, inexpensive and harmless technique that can be used bedside as frequently as needed. The aim of this article is to provide a guide for beginners about the indications, basic technical parameters, scanning technique, standardized planes, common variants and the most frequent abnormal findings visualized with this technique. This article will explain a systematic technique to adequately visualize and document all the relevant intracranial structures, using the anterior fontanelle, mastoid fontanelle and Doppler ultrasound. The variants described in this review include the normal sulcation of the premature baby, the peritrigonal echogenic "blush", lenticulostriate vasculopathy (LSV), benign enlargement of the subrarachnoid space in infancy, asymmetric lateral ventricles, connatal cysts, cavum septum pellucidum (CSP), cavum vergae (CV), cavum velum interpositum, megacisterna magna and choroid plexus cysts. This article will describe the sonographic appearance of different types of intracranial hemorrhage of the preterm and term baby, periventricular leukomalacia (PVL), central and peripheral hypoxic-ischemic events of the term baby, neonatal arterial infarction, cerebro-venous sinus thrombosis, congenital and neonatal cerebral infections, hydrocephalus, intracranial solid and cystic masses, and congenital brain malformations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.099 | 0.066 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".